Senior Data Scientist

Job description

Accountabilities Lead AI, ML, and NLP initiatives end to end, covering problem framing, solution design, model development, validation, deployment, monitoring, and ongoing production maintenance. Develop LLM-powered applications that automate complex, high-volume workflows while balancing accuracy, response time, throughput, and inference costs through model selection, caching, prompt engineering, and related techniques. Design evaluation datasets and automated evaluation pipelines to measure model quality, error rates, and performance across clinical and financial content, identifying regressions caused by changes in models, prompts, or data. Build and maintain production-grade data foundations, including ETL/ELT pipelines and feature datasets sourced from PostgreSQL transactional systems and Redshift data warehouses. Establish strong standards for data quality, reliability, documentation, lineage, and maintainability across analytical and machine learning data workflows. Collaborate with clinicians, pharmacists, and other domain experts to establish ground truth, assess edge cases, and validate model behavior against real-world clinical workflows. Ensure AI and ML solutions meet appropriate healthcare standards and regulatory requirements, including HIPAA, while monitoring for bias, safety issues, and performance drift. Serve as technical lead on complex, ambiguous projects by defining approaches, establishing best practices, setting technical direction, and influencing broader engineering and data strategy. Coach and mentor data scientists through technical pairing, design reviews, code reviews, and constructive feedback while contributing to technical hiring and team development. Communicate complex analytical results and technical concepts effectively to technical and non-technical audiences, including executive leadership, through presentations, reports, and visualizations. Partner closely with software architects and engineering teams to ensure models and data products integrate effectively into production systems and meet platform requirements. Requirements Master’s or Ph.D. in Computer Science, Data Science, Statistics, Mathematics, or a related quantitative discipline. 5+ years of professional experience in data science, machine learning, and NLP, with a demonstrated track record of delivering models into production environments. Advanced proficiency in Python and SQL. Hands-on experience developing and orchestrating ETL/ELT pipelines using technologies such as dbt, Airflow, and AWS data services including Glue, DMS, Lambda, and S3. Strong understanding of incremental data loads, idempotency, data quality testing, documentation, and data lineage. Strong software engineering fundamentals, including modular and tested production-quality Python, Git-based workflows, code reviews, Docker, and CI/CD. Experience productionizing and monitoring machine learning models in AWS environments, including model registries, ML CI/CD, versioning, and post-deployment monitoring using tools such as SageMaker and MLflow. Demonstrated experience building and evaluating LLM-powered applications, including solutions using models such as Claude through Amazon Bedrock, prompt design, retrieval and RAG pipelines, and systematic evaluation of output quality. Proven ability to independently scope ambiguous technical problems and deliver solutions from conception through production. Demonstrated project leadership and experience coaching or mentoring other data scientists. Excellent analytical, problem-solving, communication, and collaboration skills, with the ability to operate effectively in a fast-paced environment. Healthcare or insurance industry experience is preferred. Familiarity with healthcare data standards such as FHIR, HL7v2, and X12 and clinical vocabularies including RxNorm, NDC, ICD-10, and SNOMED is a plus. Experience modeling analytical data for Amazon Redshift or comparable columnar MPP platforms, extracting from PostgreSQL, and writing performant SQL against large datasets is preferred. Ability to analyze query plans and optimize slow SQL queries independently. Experience deploying containerized applications and models using Docker and cloud-managed infrastructure such as SageMaker endpoints, ECS, or Lambda; Kubernetes experience is a plus. Comfortable working primarily at a desk and traveling approximately 10% for client sites, conferences, or internal meetings. Benefits Competitive base salary of $140,000–$170,000, with exact compensation determined by skills and experience. Eligibility for a discretionary performance-based bonus. Medical, dental, and vision insurance. 401(k) eligibility after three months, with a 50% company match on the first 5% of salary contributed and a three-year vesting schedule. Health Savings Account for eligible HDHP participants, with company contributions of up to $500 for individual coverage and $1,000 for family coverage annually. 100% company-paid short- and long-term disability, AD&D, and group life insurance. 18 days of accrued PTO during the first three years, increasing thereafter. 7 paid holidays. Employee Assistance Program. Up to $1,500 annually in continuing education funding for eligible programs after one year of service. Voluntary benefits including FSA, hospital indemnity, accident, and critical illness insurance. Remote-first work environment designed to provide flexibility and support collaboration across locations. Opportunity to work on complex AI and ML challenges with significant impact on healthcare workflows and patient outcomes. How Jobgether works: We use an AI-powered matching process to ensure your application is reviewed quickly, objectively, and fairly against the role's core requirements. Our system identifies the top-fitting candidates, and this shortlist is then shared directly with the hiring company. The final decision and next steps (interviews, assessments) are managed by their internal team. We appreciate your interest and wish you the best! Why Apply Through Jobgether? Data Privacy Notice: By submitting your application, you acknowledge that Jobgether will process your personal data to evaluate your candidacy and share relevant information with the hiring employer. This processing is based on legitimate interest and pre-contractual measures under applicable data protection laws (including GDPR). You may exercise your rights (access, rectification, erasure, objection) at any time. #LI-CL1 This position is listed on behalf of a partner company, who manages all applications and next steps. Our partner is looking for a Senior Data Scientist based in the United States. This is a senior individual contributor role focused on building production-grade AI, machine learning, and NLP solutions in healthcare.You will own complex initiatives from problem definition and model development through deployment, monitoring, and long-term optimization.The role places a strong emphasis on large language models, event-driven data pipelines, and scalable machine learning systems.You will work closely with engineers, clinicians, pharmacists, and executive stakeholders to turn ambiguous challenges into measurable solutions.A key part of the position is balancing model accuracy, latency, scalability, cost, safety, and regulatory requirements across high-volume healthcare workflows.You will also influence technical direction, mentor other data scientists, and establish engineering and evaluation best practices.This is an opportunity to solve challenging problems at significant scale while helping advance intelligent healthcare technology.